SMART ROADS IN THE PERVASIVE COMPUTING LANDSCAPE

Physical objects capable of supplying computing services to users by utilizing hidden pervasive computing technologies are considered to be smart. Allowing not only a physical correspondence of object and enabling technology but also a logical one substantially increases the set of real objects to be considered as smart. This paper presents an innovative thought model of virtually smart things, i.e., objects in the real world not physically equipped with sensory gadgets or interaction facilities, but also being aware of their surrounding environment by their virtual representation. The main focus of the following sections concentrates on a Smart Road, a fully implemented use-case, telling its users where to go. 1. Pervasive Computing Landscape The pervasive and ubiquitous computing landscape is defined by an assortment of enabling technologies attached to small, embedded and mobile devices (often referred to as "smart things", "smart appliances " or "smart spaces") interacting with the user in a pro-active, autonomous, sovereign and user-authorized way [1][2]. Objects of everyda

Peanut lipids display potential adjuvanticity by triggering a pro-inflammatory response in human keratinocytes

(VLID)340146

Human-robot collaborative navigation search using social reward sources

Trabajo presentado en el 4th Iberian Robotics Conference, celebrado en Oporto (Portugal) en noviembre de 2019This paper proposes a Social Reward Sources (SRS) design for a Human-Robot Collaborative Navigation (HRCN) task: human-robot collaborative search. It is a flexible approach capable of handling the collaborative task, human-robot interaction and environment restrictions, all integrated on a common environment. We modelled task rewards based on unexplored area observability and isolation and evaluated the model through different levels of human-robot communication.The models are validated through quantitative evaluation against both agents¿ individual performance and qualitative surveying of participants¿ perception. After that, the three proposed communication levels are com-pared against each other using the previous metrics.Work supported under projects ColRobTransp (DPI2016-78957-RAEI/FEDER EU), TERRINet (H2020-INFRAIA-2017-1-two-stage-730994) and by the Spanish State Research Agency through the Maria de Maeztu Seal of Excellence to IRI (MDM-2016-0656)